diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 4725b7a..7196d6a 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -356,7 +356,7 @@ class Text2SemanticDecoder(nn.Module): x = self.ar_text_embedding(x) x = x + self.bert_proj(bert_feature.transpose(1, 2)) x = self.ar_text_position(x) - x_mask = make_pad_mask(x_lens) + x_mask = make_pad_mask_left(x_lens) y_mask = make_pad_mask(y_lens) y_mask_int = y_mask.type(torch.int64) @@ -420,7 +420,7 @@ class Text2SemanticDecoder(nn.Module): mask=xy_attn_mask, ) x_len = x_lens.max() - logits = self.ar_predict_layer(xy_dec[:, x_len:]) + logits = self.ar_predict_layer(xy_dec[:, x_len-1:]) ###### DPO ############# reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data( @@ -432,7 +432,7 @@ class Text2SemanticDecoder(nn.Module): mask=reject_xy_attn_mask, ) x_len = x_lens.max() - reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:]) + reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len-1:]) # loss # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum @@ -455,7 +455,7 @@ class Text2SemanticDecoder(nn.Module): x = self.ar_text_embedding(x) x = x + self.bert_proj(bert_feature.transpose(1, 2)) x = self.ar_text_position(x) - x_mask = make_pad_mask(x_lens) + x_mask = make_pad_mask_left(x_lens) y_mask = make_pad_mask(y_lens) y_mask_int = y_mask.type(torch.int64) @@ -502,7 +502,7 @@ class Text2SemanticDecoder(nn.Module): (xy_pos, None), mask=xy_attn_mask, ) - logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1) + logits = self.ar_predict_layer(xy_dec[:, x_len-1:]).permute(0, 2, 1) # loss # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum loss = F.cross_entropy(logits, targets, reduction="sum") @@ -578,7 +578,7 @@ class Text2SemanticDecoder(nn.Module): def pad_y_eos(self, y, y_mask_int, eos_id): targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1) # 错位 - return targets[:, :-1], targets[:, 1:] + return targets[:, :-1], targets def infer_panel_batch_infer( self,